A.5 Practical matters

A.5.1 Class ground rules

Ground rules for interactions: This course operates in a safe, respectful, and inclusive environment. That applies to class meetings, office hours, posting on discussion boards, and any other communications you send as part of this course. As a college and a community, we value diversity of age, race, ethnicity, gender identity and expression, sexual orientation, religious affiliation, disability, and other identities, visible or nonvisible.

If at any time during this course you are made to feel uncomfortable or unwelcome, I encourage you to talk to me about it. You can also use one of the other contact methods described in the resources.

Academic integrity and the honor code: You’re responsible for doing your work in this course both in accordance with stated rules (which may be different for different assignments) and with a sense of fairness and integrity. The section of the student handbook dealing with the Honor Code can be found here: https://www.mtholyoke.edu/student-handbook/student-accountability.

Very briefly, academic integrity means that you do not seek any unfair advantages over other students, and that all work you present as your own is your own. But the details of that can get complicated. I’ve tried to be very clear about what collaboration and resources are appropriate for each assignment, but if you have any questions or uncertainties about what something means, or whether something is okay, please contact me. I am very happy to talk to you about this. I am much, much happier to talk to you about it before something happens.

One issue that people tend to struggle with in this course is plagiarism. Yes, it is possible to commit plagiarism in stats or math! Sure, there are fixed vocab words and equations that everyone uses, but your explanations and discussions should always be your own creation. Any time you take someone else’s words/ideas without citing them is plagiarism – even if it’s just my notes, or the built-in R documentation, or even an AI. Again, I’m happy to talk with you about this. You can also find a helpful guide to avoiding plagiarism here: https://plagiarism.arts.cornell.edu/tutorial/index.cfm.

About AI: I have A Lot Of Thoughts about AI that we’ll talk about in class. But here’s the short version: you may use AI tools in this class, but:

  • You may not claim their work as your work. That’s plagiarism.
    • For example, you may not use AI to generate a discussion board post, or an explanation of something in your Project or a practice problem. (No, not even “just for editing.”) If you use AI for coding help, cite it, just as if you had interviewed someone or consulted a published source.
  • You may not trust them. That’s dangerous.
    • Generative AI is, basically, a very very fancy regression model that generates likely strings of text. It has no concept of truth. It will give you smooth, plausible-sounding answers that are completely wrong. If you draw on AI for anything – coding help, an explanation of an idea, anything – you must be critical, and confirm whether what it said is true.

I strongly recommend that you avoid using AI in this course. A lot of the point of the course is brain training: you are practicing so that you can understand and use ideas, both the ones in this course and the ones you’ll meet later. But for that to work, you have to use your own brain!

Prerequisites: Stat 242 or equivalent, Math 211 (linear algebra) or equivalent, and a willingness to devote regular time to working on this course.

In general, if you are ever unsure whether you’re supposed to know some bit of material already – or concerned that you don’t – I encourage you to ask me (or a classmate!) about it. Sometimes I actually don’t expect you to know it yet. And if I do expect you to know it, I’m happy to get you the help you need to pick it up.

A.5.2 Access and accommodations

My goal is to create a class that’s accessible, inclusive, and rewarding for everyone. This means accommodating everyone’s disability and accessibility needs, in addition to any logistical issues that may come up.

If anything like this applies to you, come and talk to me. Sooner is better! You can also get in touch with the folks over at Disability Services (https://www.mtholyoke.edu/directory/departments-offices-centers/disability-services) for much more help. But everyone’s needs are unique: even two students with the “same accommodations” may find that different implementations work better. So talk to me!

A.5.3 Materials

Books: There is one required “book” for this course, An Introduction to Statistical Learning (with applications in R, not Python!) by James, Witten, Hastie, and Tibshirani. Happily, it is available as a free PDF from the website https://www.statlearning.com/ . Yay! There should also be one or two copies on reserve in the library if you’re tired of looking at screens.

Electronics: You will also need a suitable electronic device. All of the work for this course should be doable via web browser; we’ll be using RStudio Server so you do not need to install R on your local device, although you can if you want to. But you will need to do a lot of typing and have multiple things open at once. I recommend you have access to an actual computer unless you’re really good with a teeny tiny keyboard, though you can certainly use your phone/tablet for some things during the course. If you have any questions or issues around devices (including accessibility issues, restrictions on screen time, not having a laptop to bring to class, etc.) let me know.